Health-related Quality of life in 640 head and neck cancer survivors after radiotherapy using EORTC QLQ-C30 and QLQ-H&N35 questionnaires

<p>Abstract</p> <p>Background</p> <p>With the advances in modern radiotherapy (RT), many patients with head and neck cancer (HNC) can be effectively cured, and their health-related quality of life (HR-QoL) has become an important issue. In this study, we evaluated the prognosticators of HR-QoL in a large cohort of HNC patients, with a focus on the result from technological advances in RT.</p> <p>Methods</p> <p>A cross-sectional investigation was conducted to assess the HR-QoL of 640 HNC patients with cancer-free survival of more than 2 years. Among them, 371 patients were treated by two-dimensional RT (2DRT), 127 by three-dimensional conformal RT (3DCRT), and 142 by intensity-modulated RT (IMRT). The EORTC QLQ-C30 questionnaire and QLQ-H&N35 module were used. A general linear model multivariate analysis of variance was used to analyze the prognosticators of HR-QoL.</p> <p>Results</p> <p>By multivariate analysis, the variables of gender, annual family income, tumor site, AJCC stage, treatment methods, and RT technique were prognosticators for QLQ-C30 results, so were tumor site and RT technique for H&N35. Significant difference (<it>p </it>< 0.05) of HR-QoL outcome by different RT techniques was observed at 2 of the 15 scales in QLQ-C30 and 10 of the 13 scales in H&N35. Compared with 2DRT, IMRT had significant better outcome in the scales of global QoL, physical functioning, swallowing, senses (taste/smell), speech, social eating, social contact, teeth, opening mouth, dry mouth, sticky saliva, and feeling ill.</p> <p>Conclusions</p> <p>The technological advance of RT substantially improves the head-and-neck related symptoms and broad aspects of HR-QoL for HNC survivors.</p

The Different Dose-Volume Effects of Normal Tissue Complication Probability Using LASSO for Acute Small-Bowel Toxicity during Radiotherapy in Gynecological Patients with or without Prior Abdominal Surgery

Purpose. To develop normal tissue complication probability (NTCP) model with least absolute shrinkage and selection operator (LASSO) to analyze dose-volume effects that influence the incidence of acute diarrhea among gynecological patients with/without prior abdominal surgery. Methods and Materials. Ninety-five patients receiving gynecologic radiotherapy (RT) were enrolled. The endpoint was defined as the grade 2+ acute diarrhea toxicity during treatment. We obtained the range of small-bowel volume in V4 Gy to V40 Gy of dose. Results. The number of patients experiencing grade 2+ acute diarrhea toxicity was 23/61 (38%) in the group without abdominal surgery (group 0) and 17/34 (50%) patients with abdominal surgery (group 1). The most significant predictor was found for the logistic NTCP model with V16 Gy as the cutoff dose for group 0 and V40 Gy for group 1. Logistic regression NTCP model parameters were TV10 ≈ 290 cc for V16 Gy and TV10 ≈ 75 cc for V40 Gy, respectively. Conclusion. To keep the incidence of grade 2+ acute small-bowel toxicity below 10%, we suggest that small-bowel volume above the prescription dose (V16 Gy) should be held to <290 cc for patients without abdominal surgery, and the prescription dose (V40 Gy) should be maintained <75 cc for patients with abdominal surgery

3-D Medical Image Processing Combining Wavelet Edge Detection and Segmentation

[[abstract]]由於傳統的電腦斷層攝影（CT）、正子掃描（PET）或核磁共振造影掃描（MRI）所產生的醫學影像，本質上都是切片結構的二維影像，因此組成三維描繪亦必須依據此一特性來實現。如何有效地堆疊二維影像組成三維描繪，達成邊界正確以及最少計算時間的目標，必須先審慎選擇二維影像的切割處理。然而沒有任何一種切割的演算法可以在任何情況下均達到理想，因為每一個方法都有其優點與缺點。有一個最近的研究提出使用熵值最大化的觀念來結合兩個分別來自梯度邊緣偵測，以及來自區域影像切割的方法，可以結合影像切割與邊緣尋找之優點，應是最好的方法。此一方法結合兩者之資訊特徵，使得三維影像處理得到更完善的結果。在本研究中使用了五組真實的CT與MRI醫學影像來驗證此一方法，結果發現我們的三維醫學影像處理之效果比其他現有的方法更令人滿意。[[abstract]]Since conventional Computed Tomography (CT), Positron Emission Tomography (PET), or Magnetic Resonance Imaging (MRI) medical images are all slice-based two-dimensional (2-D) images, three-dimensional (3-D) rendering for medical images deserves a unique treatment according to this particular feature. To effectively stacking 2-D images to form 3-D images involves how to reconstruct the object correctly and with minimal time. Therefore 2-D segmentation method must be carefully chosen. But no single technique can claim to achieve consistent results under all circumstances, they all has its advantages and disadvantages. A recent method known as the entropy maximization procedure proposed to combine both gradient and region segmentation approaches to achieve a better result seemed to be the best way. It allows us to utilize all available information to achieve the most robust segmentation results for 3-D image processing. Five examples of true CT scan images and MRI data were used to test the validity of this method. We found our combined 3-D segmentation method is indeed superior to other available 3-D image processing methods

Hourly Power Consumption Forecasting Using RobustSTL and TCN

Power consumption forecasting is a crucial need for power management to achieve sustainable energy. The power demand is increasing over time, while the forecasting of power consumption possesses challenges with nonlinearity patterns and various noise in the datasets. To this end, this paper proposes the RobustSTL and temporal convolutional network (TCN) model to forecast hourly power consumption. Through the RobustSTL, instead of standard STL, this decomposition method can extract time series data despite containing dynamic patterns, various noise, and burstiness. The trend, seasonality, and remainder components obtained from the decomposition operation can enhance prediction accuracy by providing significant information from the dataset. These components are then used as input for the TCN model applying deep learning for forecasting. TCN employing dilated causal convolutions and residual blocks to extract long-term data patterns outperforms recurrent networks in time series forecasting studies. To assess the proposed model, this paper conducts a comparison experiment between the proposed model and counterpart models. The result shows that the proposed model can grasp the rules of historical time series data related to hourly power consumption. Our proposed model overcomes the counterpart schemes in MAPE, MAE, and RMSE metrics. Additionally, the proposed model obtains the best results in precision, recall, and F1-score values. The result also indicates that the predicted data can fit the pattern of the actual data

Hourly Power Consumption Forecasting Using RobustSTL and TCN

Power consumption forecasting is a crucial need for power management to achieve sustainable energy. The power demand is increasing over time, while the forecasting of power consumption possesses challenges with nonlinearity patterns and various noise in the datasets. To this end, this paper proposes the RobustSTL and temporal convolutional network (TCN) model to forecast hourly power consumption. Through the RobustSTL, instead of standard STL, this decomposition method can extract time series data despite containing dynamic patterns, various noise, and burstiness. The trend, seasonality, and remainder components obtained from the decomposition operation can enhance prediction accuracy by providing significant information from the dataset. These components are then used as input for the TCN model applying deep learning for forecasting. TCN employing dilated causal convolutions and residual blocks to extract long-term data patterns outperforms recurrent networks in time series forecasting studies. To assess the proposed model, this paper conducts a comparison experiment between the proposed model and counterpart models. The result shows that the proposed model can grasp the rules of historical time series data related to hourly power consumption. Our proposed model overcomes the counterpart schemes in MAPE, MAE, and RMSE metrics. Additionally, the proposed model obtains the best results in precision, recall, and F1-score values. The result also indicates that the predicted data can fit the pattern of the actual data

Developing Multivariable Normal Tissue Complication Probability Model to Predict the Incidence of Symptomatic Radiation Pneumonitis among Breast Cancer Patients.

Symptomatic radiation pneumonitis (SRP), which decreases quality of life (QoL), is the most common pulmonary complication in patients receiving breast irradiation. If it occurs, acute SRP usually develops 4-12 weeks after completion of radiotherapy and presents as a dry cough, dyspnea and low-grade fever. If the incidence of SRP is reduced, not only the QoL but also the compliance of breast cancer patients may be improved. Therefore, we investigated the incidence SRP in breast cancer patients after hybrid intensity modulated radiotherapy (IMRT) to find the risk factors, which may have important effects on the risk of radiation-induced complications.In total, 93 patients with breast cancer were evaluated. The final endpoint for acute SRP was defined as those who had density changes together with symptoms, as measured using computed tomography. The risk factors for a multivariate normal tissue complication probability model of SRP were determined using the least absolute shrinkage and selection operator (LASSO) technique.Five risk factors were selected using LASSO: the percentage of the ipsilateral lung volume that received more than 20-Gy (IV20), energy, age, body mass index (BMI) and T stage. Positive associations were demonstrated among the incidence of SRP, IV20, and patient age. Energy, BMI and T stage showed a negative association with the incidence of SRP. Our analyses indicate that the risk of SPR following hybrid IMRT in elderly or low-BMI breast cancer patients is increased once the percentage of the ipsilateral lung volume receiving more than 20-Gy is controlled below a limitation.We suggest to define a dose-volume percentage constraint of IV20< 37% (or AIV20< 310cc) for the irradiated ipsilateral lung in radiation therapy treatment planning to maintain the incidence of SPR below 20%, and pay attention to the sequelae especially in elderly or low-BMI breast cancer patients. (AIV20: the absolute ipsilateral lung volume that received more than 20 Gy (cc)